In [11]:
def evaluate_model(model, dataloader):
model.eval() # Set the model to evaluation mode
all_preds = []
all_labels = []
with torch.no_grad(): # Disable gradient calculation
for inputs, labels in dataloader:
inputs = inputs.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.numpy())
return all_preds, all_labels
# Get predictions
val_preds, val_labels = evaluate_model(trained_model, dataloaders['validation'])
# Calculate accuracy
accuracy = sum(np.array(val_preds) == np.array(val_labels)) / len(val_labels)
print(f"Validation Accuracy: {accuracy:.4f}")
# Visualize some predictions
def plot_results(images, preds, labels):
fig, axs = plt.subplots(5, 6, figsize=(20, 20))
for i, (img_path, pred, label) in enumerate(zip(image_datasets['validation'].imgs, preds, labels)):
img = plt.imread(img_path[0])
ax = axs[i // 6, i % 6]
ax.imshow(img)
ax.axis('off')
color = 'green' if pred == label else 'red'
ax.set_title(f"Pred: {'Chihuahua' if pred == 0 else 'Muffin'}\nTrue: {'Chihuahua' if label == 0 else 'Muffin'}", color=color)
plt.tight_layout()
plt.show()
plot_results(image_datasets['validation'].imgs, val_preds, val_labels)
Validation Accuracy: 0.9000
